An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

Shamout, Farah E.; Shen, Yiqiu; Wu, Nan; Kaku, Aakash; Park, Jungkyu; Makino, Taro; Jastrzkebski, Stanislaw; Wang, Duo; Zhang, Ben; Dogra, Siddhant; Cao, Meng; Razavian, Narges; Kudlowitz, David; Azour, Lea; Moore, William; Lui, Yvonne W.; Aphinyanaphongs, Yindalon; Fernandez-Granda, Carlos; Geras, Krzysztof J.

During the COVID-19 pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images, and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an AUC of 0.786 (95% CI: 0.742-0.827) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions, and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at NYU Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.